Movement amount estimation device, movement amount estimation method, and computer-readable recording medium storing movement amount estimation program

ABSTRACT

A movement-amount-estimation device includes an edge-removal unit to generate a first edge-removed image by removing from a first edge-image a first-removal-target edge by on a change in brightness indicated by edge intensity in a direction from an inner side to an outer side of a mobile body, the first edge-image being generated from a first image taken by an imaging device mounted on the mobile body, and to generate a second-edge-removed image by removing from a second edge-image a second-removal-target edge by the change in brightness indicated by the edge intensity in the direction from the inner side to the outer side, the second edge-image being generated from a second image taken at a different clock time than the first image; and an output unit configured to output a movement amount of the mobile body that is estimated based on the first edge-removed image and the second edge-removed image.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2014-078212, filed on Apr. 4,2014, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein are related to a movement amountestimation device, a movement amount estimation method, and acomputer-readable recording medium storing movement amount estimationprogram.

BACKGROUND

There has been known a technology for mounting a camera configured toimage the outside on a mobile body such as a vehicle, taking a pictureof the surroundings including a road surface, and estimating a movementamount of the mobile body from a movement amount of a pattern on theroad surface in the images taken at two different clock times (see,“Development of a Monocular Ranging and Verification System Using anOnboard Camera”, SEI Technical Review, Issue No. 169, pp. 82 to 87, July2006, for example). In such a technology, a movement amount may not beestimated with high accuracy due to an influence such as a shade of theown vehicle, for example, when a lane boundary position or a solidobject is detected from the images taken, and the like. Thus, there hasalso been known a technology for improving detection accuracy of a laneboundary position or a solid object (see, Japanese Laid-open PatentPublication Nos. 2003-337999, 2009-265783, 2010-15367, 2004-338637, and09-72716, for example).

SUMMARY

According to an aspect of the invention, a movement amount estimationdevice includes an edge removal unit configured to generate a firstedge-removed image by removing a first removal target edge based on achange in brightness indicated by edge intensity in a direction from aninner side to an outer side of a mobile body from a first edge image,the first edge image being generated from a first image taken by animaging device mounted on the mobile body, and to generate a secondedge-removed image by removing a second removal target edge based on thechange in brightness indicated by the edge intensity in the directionfrom the inner side to the outer side of the mobile body from a secondedge image, the second edge image being generated from a second imagetaken at a different clock time than the first image; and an output unitconfigured to output a movement amount of the mobile body that isestimated based on the first edge-removed image and the secondedge-removed image.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating one example of a functionalconfiguration of a movement amount estimation device according to afirst embodiment;

FIG. 2 is a view illustrating one example of processing by an edgeremoval unit of the movement amount estimation device according to thefirst embodiment;

FIG. 3 is a block diagram illustrating one example of a functionalconfiguration of a movement amount estimation device according to asecond embodiment;

FIG. 4 is a view illustrating one example of a road surface projectionimage according to the second embodiment;

FIG. 5 is a view illustrating another example of the road surfaceprojection image according to the second embodiment;

FIG. 6 is a view illustrating one example of a pair-edge exclusionmethod in the road surface projection image according to the secondembodiment;

FIG. 7 is a view illustrating one example of a self-shade removalprocess according to the second embodiment;

FIG. 8 is a view illustrating one example of movement amount estimationaccording to the second embodiment;

FIG. 9 is a view illustrating one example of a mobile body coordinatesystem according to the second embodiment;

FIG. 10 is a view illustrating a road surface coordinate systemaccording to the second embodiment;

FIG. 11 is a view illustrating one example of a camera position andposture according to the second embodiment;

FIG. 12 is a view illustrating one example of a road surface projectionimage coordinate system according to the second embodiment;

FIG. 13 is a view illustrating one example of a camera coordinate systemaccording to the second embodiment;

FIG. 14 is a view illustrating one example of an edge list according tothe second embodiment:

FIG. 15 is a view illustrating one example of a predetermined areasetting method according to the second embodiment;

FIG. 16 is a flow chart illustrating one example of main operations of amovement amount estimation device according to the second embodiment;

FIG. 17 is a flow chart illustrating one example of a removal candidateedge extraction process according to the second embodiment;

FIG. 18 is a flow chart illustrating one example of a pair-edgeexclusion process according to the second embodiment;

FIG. 19 is a flow chart illustrating one example of a removal targetedge removal process according to the second embodiment;

FIG. 20 is a view illustrating a predetermined area setting exampleaccording to a variation example of the second embodiment;

FIG. 21 is a view illustrating a predetermined area setting exampleaccording to a third embodiment;

FIG. 22 is a flow chart illustrating one example of a pair-edgeexclusion method in the predetermined area setting example according tothe third embodiment;

FIG. 23 is a view illustrating one example of a hardware configurationof a movement amount estimation device according to a fourth embodiment;

FIG. 24 is a block diagram illustrating one example of a functionalconfiguration of the movement amount estimation device according to thefourth embodiment; and

FIG. 25 is a view illustrating one example of a hardware configurationof a standard computer.

DESCRIPTION OF EMBODIMENTS

However, in the related art, for images at two clock times which areused to estimate a movement amount, images taken in a short period oftime which seem to include a common road surface pattern are used. Thus,in the images at the two clock times, a shade of an own vehicle is oftenfixed relative to a position of the own vehicle. Accordingly, a matchingdegree of shades is high in the images at two time clocks which includethe shade of the own vehicle. When a movement amount is estimatedthrough matching of images, the movement amount may be estimated as zeroalthough a vehicle has actually moved. In addition, a road surfacepattern and the like may not be detected with high accuracy due to theshade of the own vehicle. As such, in images used to estimate a movementamount, influence of a shade of an own vehicle is complicated, and insome cases, it is difficult to remove the influence with theconventional technologies as described above.

Accordingly, it is desired to make it possible to estimate a movementamount of a mobile body with high accuracy even when a shade of themobile body is present in an image.

First Embodiment

A movement amount estimation device 1 according to a first embodiment isdescribed hereinafter with reference to the drawings. FIG. 1 is a blockdiagram illustrating one example of a functional configuration of themovement amount estimation device 1 according to the first embodiment.The movement amount estimation device 1 is a device configured toreceive two edge images generated based on images which include a mobilebody and are acquired at two different clock times, and to output amovement amount of the mobile body.

As illustrated in FIG. 1, the movement amount estimation device 1includes an edge removal unit 3 and an output unit 5. The edge removalunit 3 is configured to generate an edge-removed image by removingremoval target edges, based on a change in brightness indicated by edgeintensity in a direction from the inner side to the outer side of amobile object, in an edge image generated from an image taken by animaging unit mounted on the mobile body. Here, the edge image is animage which is represented by a characteristic amount calculated basedon a pixel value in the image taken by the imaging unit. The edge imageis generated by performing differential processing on the image, forexample. The characteristic amount calculated by the differentialprocessing is the edge intensity, for example.

The output unit outputs an estimated movement amount that is estimatedbased on a first edge-removed image and a second edge-removed image. Thefirst edge-removed image is an image that is generated from a firstimage taken by the imaging unit mounted on the mobile body, and fromwhich the removal target edges are removed by the edge removal unit 3based on the change in brightness viewed in the direction from the innerside to the outer side of the mobile body. The second edge-removed imageis generated from a second edge image that is generated from a secondimage taken at a different clock time from the first image. Morespecifically, the second edge-removed image is an image from whichsecond removal target edges are removed by the edge removal unit 3,based on the change in brightness in the direction from the inner sideto the outer side of the mobile body, in the second edge image. Adirection of brightness change, which is indicated by the edge intensityon an edge image, from the side close to the center of a mobile body tothe side far from the center of the mobile body, is hereinafter referredto as a brightness change direction. In addition, the direction from theinner side (side close to the center of the mobile body) to the outerside (side far from the center of the mobile body) of the mobile body isreferred to as a radial direction.

FIG. 2 is a view illustrating one example of processing by the edgeremoval unit 3 of the movement amount estimation device 1 according tothe first embodiment. Hereinafter, image of a mobile body or the like inan image is referred as to a mobile body on an image or included in animage as appropriate.

As illustrated in FIG. 2, a first image P1 is a road surface projectionimage including a mobile body 10. The first image P1 is a road surfaceprojection image including the mobile body 10. The first image P1 mayalso be generated based on images captured by a plurality of camerasprovided on the mobile body 10. A white line 16 is imaged in the firstimage P1. A self-shade 12 of the mobile body 10 is also imaged. Aself-shade is a shade of a mobile body made by illuminating light orsunlight.

An edge image 20 is an image generated by applying differentialprocessing with a Sobel filter, for example, on the first image P1. Theedge image 20 includes edges 14 which include a plurality of edges bythe self-shade 12. In the following, when a line segment is referred toas an edge as in the edges 14, the edge shall generically mean aplurality of edges in the vicinity of that line segment.

A radial direction 22 illustrates an example of a direction from thecenter of the mobile body 10 to the outer side of the mobile body 10.The edge removal unit 3 removes removal target edges judged to beremoved based on the bright change direction of the radial direction 22from the center of the mobile body 10 to the outer side. The removaltarget edges may be edges on which the brightness change direction ofthe radial direction is a direction from darkness to brightness. Thebrightness change direction may also be judged based on a degree of edgeintensities of the edges in the edge image 20, for example, in twodirections which are orthogonal to each other.

An edge-removed image 25 represents an example of an image in which theremoval target edges are removed by the edge removal unit 3. Theedge-removed image 25 is an example in which edges whose brightnesschange direction in the radial direction 22 indicates a direction fromdarkness to brightness (hereinafter referred to as a darkness-brightnessdirection) are removed. In the edge-removed image 25, removal targetedges 17 are removed. On the one hand, some edges 18 of the white line16 remain. The edges 14 of the self-shade 12 in the edge image 20indicate the darkness-brightness direction which shifts from a dark areato a bright area because the edges 14 shift from the self-shade to theroad surface when viewed in the radial direction 22. Thus, the edges 14are the removal target edges. Therefore, in the edge-removed image 25,while the edges 14 by the self-shade 12 are removed, at least a part ofthe edges of the white line 16 is not removed. Accordingly, a movementamount may be estimated by using edge-removed images generated based onimages which are taken at two different clock times and include a mobilebody.

As described above, with the movement amount estimation device 1according to the first embodiment, a self-shade may be removed in anedge image by removing removal target edges based on the brightnesschange direction viewed from the center of the mobile body 10 to theradial direction. In addition, in the edge-removed image 25, like theedges 18, at least a part of the edge associated with the white line 16that is desirable when a movement amount is estimated is not removed.

As described above, with the movement amount estimation device 1according to the first embodiment, a movement amount may be estimated byperforming image matching of two edge-removed images which are eachgenerated based on images taken at two different clock times. In thismanner, a self-shade of a mobile body is removed in an edge-removedimage. Thus, even when a self-shade by illuminating light is imaged intaken images, a movement amount may be estimated with high accuracy andwithout being adversely affected by the self-shade. More specifically,when a movement amount is estimated, any effect of a self-shade isremoved while at least a part of edges associated with a white line on aroad surface remains.

In the movement amount estimation device 1, as a self-shade property isused a property that a brightness-darkness pattern of an edge at aborder between a self-shade and a road surface is in a direction fromdarkness to brightness in a road surface projection image when viewedfrom a neighborhood of the center of the mobile body to the radialdirection. Since an edge whose brightness change direction is darknessto brightness is extracted in this manner, and no time-series data isused when a self-shade is removed, the self-shade may be removed from aroad surface projection image at one clock time, irrespective of achange in shade positions. As the self-shade property, the self-shadescarcely changes in a shape/position in a short period of time. However,by removing a self-shade from an image, when a movement amount isestimated through image matching, a cause of error that a movementamount is judged as zero although a mobile body moves may be removedbecause of a high matching degree using the self-shade.

On the one hand, when a self-shade is removed by utilizing the propertythat a self-shade makes little movement even when a mobile body movesand removing a motionless part from an image, a part of a road surfacepattern which is parallel to a traveling direction may be misidentifiedas a self-shade. However, the movement amount estimation device 1according to the first embodiment may avoid excessive removal ofcharacteristics of the road surface pattern attributable tomisidentification of some of the road surface pattern as a self-shade.

Second Embodiment

A movement amount estimation device 50 according to a second embodimentis described hereinafter. Same signs are assigned to a configuration andoperation similar to the movement amount estimation device 1 accordingto the first embodiment and an overlapping description is omitted.

FIG. 3 is a block diagram illustrating one example of a functionalconfiguration of the movement amount estimation device 50 according tothe second embodiment. The movement amount estimation device 50according to the second embodiment includes an edge removal unit 30 inplace of the edge removal unit 3 of the movement amount estimationdevice 1. In addition, similar to the movement amount estimation device1, the movement amount estimation device 50 includes an output unit 5.The edge removal unit 30 further includes a removal candidate edgeextraction unit 51, a pair-edge exclusion unit 53, a pair exclusionremoval unit 55, and a storage unit 57.

The removal candidate edge extraction unit 51 is configured to extractremoval target edges in an edge image based on the brightness changedirection viewed in the radial direction. For example, in the edgeimage, the removal candidate edge extraction unit 51 extracts as aremoval candidate edge an edge whose brightness change direction viewedin the radial direction is in the direction of darkness to brightness.The removal candidate edge is a candidate edge for a removal target edgewhich is actually a target of removal at the stage in which the removalcandidate edge is extracted.

The pair-edge exclusion unit 53 is configured to exclude a pair-edgefrom edges that are judged as a removal candidate edge by the removalcandidate edge extraction unit 51. The pair-edge is a removal candidateedge extracted in an edge image, and refer to edge that have an edgejudged to mutually offset a change in brightness when paired withanother edge. Mutually offsetting a change in brightness refers to, forexample, a state in which an edge, such as the removal candidate edge,in the darkness-brightness direction and an edge in thebrightness-darkness direction each of which have an almost same amountof change in brightness are present in a predetermined area. Forexample, the pair-edge is judged as, for example, a removal candidateedge which is associated with an edge in the neighborhood of the removalcandidate edge, where the brightness change direction of the edge viewedfrom the center of a mobile body to the radial direction is a directionfrom brightness to darkness (hereinafter referred to as abrightness-darkness direction) and an amount of change in brightness ofthe edge is almost same to that of the removal candidate edge.

In processing of the pair-edge exclusion unit 53, it is intended not toremove an edge associated with a white line drawn on a road surface whenan edge associated with a self-shade is removed from an edge image, byexcluding the pair-edge from removal candidate edges. Namely, acharacteristic is considered that white lines on a road surface areoften drawn at certain width and the brightness change direction ispresent as a pair of the darkness-brightness direction and thebrightness-darkness direction. Whether or not an edge is a pair-edge isjudged based on the edge intensity of an edge included in apredetermined area including a removal target edge, and a correspondingbrightness change direction, for example. Details of a method forjudging whether or not edges are pair-edges are described below.

The pair exclusion removal unit 55 is configured to remove from an edgeimage removal target edges in which a pair-edge is removed from removalcandidate edges by the pair-edge exclusion unit 53. With this, apair-exclusion-edge-removed image from which an edge associated with aself-shade is removed may be obtained. Then, since pair-edge associatedwith a white line is not a target of removal, removal of the pair-edgefrom the pair-exclusion-edge-removed image is avoided.

The output unit 5 outputs a movement amount which is estimated based ontwo pair-exclusion-edge-removed images that are based on images taken atdifferent clock times. The storage unit 57 is a storage device whichstores, for example, a taken image, or an edge image of the taken image,and an edge list 190 to be described below.

FIG. 4 is a view illustrating a road surface projection image 80according to the second embodiment. Darkness-brightness direction edges86 are depicted in the road surface projection image 80. A mobile body82 and a self-shade 84 are also included in the road surface projectionimage 80. The darkness-brightness direction edges 86 represent aplurality of edges associated with a self-shade 84 and is generated byperforming, for example, differentiation processing on a road surfaceprojection image including the mobile body 82. A center 83 representsthe center of the mobile body 82, for example. A radial direction 88 isa direction from the center 83 to the outer side of the road surfaceprojection image 80. Viewed in the radial direction 88, thedarkness-brightness direction edges 86 are edges whose brightnesschanges in a direction from darkness to brightness. Note that details ofan example of judgment on the brightness change direction are describedbelow.

FIG. 5 is a view illustrating a road surface projection image 100according to the second embodiment. The road surface projection image100 includes a white line 102 and another vehicle 110, in addition tothe mobile body 82. A darkness-brightness direction edge 104 and abrightness-darkness direction edge 106 are edges associated with thewhite line 102. The other vehicle 110 has a shade 112. Abrightness-darkness direction edge 114 is an edge associated with theshade 112. As just described, in the shade 112 of the other vehicle 110,since the associated edge is the brightness-darkness direction edge 114,the shade 112 of the other vehicle 110 is not removed even when thedarkness-brightness direction edge viewed in the radial direction 88 isremoved. For the white line 102, it is illustrated that thedarkness-brightness direction edge 104 and the brightness-darknessdirection edge 106 are a pair in a range including the white line 102.Thus, in the pair-exclusion-edge-removed image, the white line 102remains by causing the pair exclusion removal unit 55 to exclude thepair-edges from the removal candidate edges.

FIG. 6 is a view illustrating one example of a pair-edge exclusionmethod in a road surface projection image 120 according to the secondembodiment. The road surface projection image 120 includes a white line122, a white line 130, and another shade 136, in addition to the mobilebody 82. A darkness-brightness direction edge 124 and abrightness-darkness direction edge 126 are edges associated with thewhite line 122. A darkness-brightness direction edge 132 and abrightness-darkness direction edge 134 are edges associated with thewhite line 130. A brightness-darkness direction edge 138 is an edgeassociated with the other shade 136.

Predetermined areas 140, 142, 144 are depicted in the road surfaceprojection image 120. In this example, the predetermined areas 140, 142,144 are rectangular areas having sides parallel to the verticaldirection of the road surface projection image 120 and sides parallel tothe horizontal direction. The predetermined area 140 is associated withone of the darkness-brightness direction edges 86 of the self-shade 84of the mobile body 82, and set to include the edge. The edge is referredto as a set edge of the predetermined area 140. The predetermined area140 is illustrated as an example of a predetermined area whose set edgeis not a pair-edge associated with the white line. More specifically, inthe predetermined area 140, it is judged that the set edge is notassociated with an edge to be a pair-edge which is offset by the setedge in the predetermined area 140.

The predetermined area 142 is an example of an area including thedarkness-brightness direction edges 124 and the brightness-darknessdirection edges 126. The predetermined area 142 is set in associationwith a set edge of the darkness-brightness direction edges 124. In thepredetermined area 142, when it is judged that the set edge of thedarkness-brightness direction edges 124 and any edge in thepredetermined area 142 are mutually offset, it is judged that the setedge associated with the predetermined area 142 is judged as apair-edge.

Also in the predetermined area 144, when it is also judged that a setedge of the darkness-brightness direction edges 132 is paired with anyedge in the predetermined area 144, the set edge of the predeterminedarea 144 is judged as a pair-edge. As described above, a predeterminedarea is set for each of extracted removal target edges, and it is judgedin each of the predetermined areas whether or not a set edge is apair-edge. Details of a method for judging a pair-edge are describedbelow.

FIG. 7 is a view illustrating one example of a self-shade removalprocess according to the second embodiment. As illustrated in FIG. 7,for example, a mobile body 10, a self-shade 12, and a white line 16 on aroad surface are included in a first image P1. An edge image 20 isgenerated by performing differential processing on the first image P1.Edge 14 and edges 27 to 29 are included in the edge image 20. The edge14 corresponds to a border from the self-shade 12 to the road surface.The edges 27 to 29 correspond to the borders between the white line 16and the road surface.

Using the edge image 20, there are extracted removal candidate edges inthe darkness-brightness direction viewed in the radial direction fromthe center of the mobile body 10 to the outer side. A pair-edgeassociated with an edge of the brightness-darkness direction is excludedfrom the extracted removal candidate edges. Thus, a removal target edgeimage 150 is generated. In the removal target edge image 150, the edge14 is a removal target edge, while the edges 27 to 29 are excluded aspair-edges.

The removal target edge image 150 is used to remove the edge 14 of theself-shade 12 from the edge image 20. More specifically, the removaltarget edges remaining in the removal target edge image 150 are removedfrom the edge image 20, and a pair-exclusion-edge-removed image 155 isgenerated. The movement amount estimation device 50 estimates a movementamount based on two pair-exclusion-edge-removed images, such as thepair-exclusion-edge-removed image 155, which are generated from imagestaken at two different clock times.

FIG. 8 is a view illustrating one example of movement amount estimationaccording to the second embodiment. As illustrated in FIG. 8, a firstimage P1 and a second image P2 are used in the movement amountestimation device 50. The first image P1 and the second image P2 areroad surface projection images which are each generated based on cameraimages of the surroundings taken at different clock times by at leastone camera provided on a mobile body. Furthermore, for each of the firstimage P1 and the second image P2, a pair-exclusion-edge-removed image155-1 and a pair-exclusion-edge-removed image 155-2 are generatedthrough generation of the edge image and the removal target edge image,as described above. A movement amount 152 is estimated by matching thesepair-exclusion-edge-removed images. Then, since a self-shade 12 and aself-shade 13 have been removed, as in an area 154, the image matchingis performed with high accuracy based on a white line 16 and a whiteline 19.

On the one hand, as a comparative example is described a case in whichimage matching of the first image P1 and the second image P2 isperformed without using the pair-exclusion-edge-removed image 155. Insuch a case, as in an area 156, the self-shade 12 and the self-shade 13in the two images are almost overlapping. Thus, for example, in imagematching, as in a self-shade 158, the self-shade 12 and the self-shade13 in the two images match, which makes it difficult to estimate anaccurate amount of movement. In such a case, is also possible a methodfor attempting to remove the self-shade 12 by utilizing the fact thatthe self-shade 12 scarcely moves and removing a motionless part from theimage. However, the method for removing a motionless part even removes aroad surface pattern parallel to a traveling direction, which makes itdifficult to estimate an accurate amount of movement. With the movementamount estimation device 50 according to the second embodiment, sincethe white line 16 and the white line 19 are excluded from the removaltargets, a movement amount may be estimated with high accuracy.

Estimation of a movement amount according to the second embodiment isfurther described in detail hereinafter. The first image P1 and thesecond image P2 used in the movement amount estimation device 50 aregenerated by taking images of the surroundings at different clock timesby at least one or more camera provided on the mobile body andsynthesizing the images into road surface projection images. Then,arrangement of the at least one or more camera may be stored in advancein the storage unit 57.

Here, coordinates adopted in the movement amount estimation device 50according to the second embodiment is described. FIG. 9 is a viewillustrating one example of a mobile body coordinate system. Asillustrated in FIG. 9, the mobile body coordinate system O-XYZ is athree-dimensional coordinate system in which a mobile body 160 is usedas a reference. An origin O of the mobile body coordinate system is apoint on a road surface 162 directly under the center of the mobile body160. An X axis is a coordinate axis from the origin O to the right ofthe mobile body 160. A Y axis is a coordinate axis from the origin O toa front direction of the mobile body 160. A Z axis is a coordinate axisfrom the origin O to a upward direction of the road surface 162. FIG. 10is a view illustrating a road surface coordinate system. As illustratedin FIG. 10, the road surface coordinate system O-XY is defined on an XYplane of the mobile body coordinate system.

FIG. 11 is a view illustrating one example of a camera position andposture. As illustrated in FIG. 11, a position and posture of an m-thcamera 164 (m is an integer of more than or equal to 1) mounted on themobile body 160 may be represented by a translation vector Tm and arotating matrix Rm in the mobile body coordinate system O-XYZ.

FIG. 12 is a view illustrating one example of a road surface projectionimage coordinate system. As illustrated in FIG. 12, the road surfaceprojection image coordinate system o-xy is a two-dimensional coordinatesystem of a road surface projection image 166. An origin o of the roadsurface projection image 166 may be an upper left apex of the roadsurface projection image 166, for example. Size of the road surfaceprojection image 166 in the x axis direction is wx and size in the yaxis direction is wy. Note that the size wx may be the number of pixelsin the x axis direction and the size wy may be the number of pixels inthe y axis direction. When coordinates of the center 168 of the roadsurface projection image 166 is (cx, cy), the following expression 1 andexpression 2 may be formed.

cx=wx/2  (Expression 1)

cy=wy/2  (Expression 2)

FIG. 13 is a view illustrating one example of a camera coordinatesystem. A camera image coordinate system px-py of FIG. 13 is atwo-dimensional coordinate system of a camera image 170 on an imagedsurface of the camera 164. A px axis is a coordinate axis in ahorizontal direction of the camera image 170 and a py axis is acoordinate axis in a vertical direction of the camera image 170.

A camera coordinate system QX-QY-QZ is a three-dimensional coordinatesystem having the center 172 of the camera image 170 as an origin. A QZaxis is on an optical axis of the camera 164, and a QX axis and a QYaxis are on a plane perpendicular to the optical axis. Connecting apoint 174 and a point 180 with a straight line 178, the straight line178 intersects with the camera image 170 at an intersection point 176,where the point 174 is a point away from the center 172 in a negativedirection of the QZ axis for a focal distance f of the camera 164 andthe point 180 is a point on an object as an imaging target of the camera164. Then, the point 180 on the object is projected to the intersectionpoint 176 on the camera image 170.

Assume that coordinates of the point 180 on the object are (QX, QY, QZ),angle made by the QZ axis and the straight line 178 is angle 8, andangle made by the QX axis and a straight line 182 connecting the center172 with the intersection point 176 is angle φ. Assume that coordinatesof the center 172 in the camera image coordinate system px-py are (vx,vy), coordinates of the point 176 on the camera image 170 are (px, py),and a pixel value of pixels corresponding to the point 176 is pm (px,py). In addition, assume that actual size on the road surface 162corresponding to size in the x axis direction of one pixel of the roadsurface projection image 166 is MX, and actual size on the road surface162 corresponding to size in the y direction of one pixel of the roadsurface projection image 166 is MY.

Removal candidate edge extraction is described hereinafter. When an edgeimage is generated based on a generated road surface projection image, ageneral differential operator such as Sobel, for example, is caused toact on the road surface projection image to determine edge intensitycorresponding to each pixel. For example, in the embodiment, assume thata Sobel filter expressed by the following expression 3 and expression 4is used as a differential operator. Note that a filter Sh is a filterwhich calculates horizontal edge intensity Eh in the x axis direction inthe road surface projection image, for example. A filter Sv is a filterwhich calculates vertical edge intensity Ev in the y axis direction inthe road surface projection image, for example.

$\begin{matrix}{{Sh} = \begin{pmatrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{pmatrix}} & \left( {{Expression}\mspace{14mu} 3} \right) \\{{Sv} = \begin{pmatrix}{- 1} & {- 2} & {- 1} \\0 & 0 & 0 \\1 & 2 & 1\end{pmatrix}} & \left( {{Expression}\mspace{14mu} 4} \right)\end{matrix}$

FIG. 14 is a view illustrating one example of an edge list 190. The edgelist 190 is generated corresponding to each pixel in the edge image 20,for example. In FIG. 14, edge lists 190-1, 190-2, . . . , 190-n (n isthe number of pixels in the edge image) are expressed corresponding toeach edge e(1,1), e(2, 1), . . . , e(j, i), . . . . The edge lists190-1, 190-2, . . . , 190-n are also referred to as the edge list 190collectively or as a representative. Note that a variable i is avariable indicating the number of pixels in the vertical direction ofthe edge image and a variable j is a variable indicating the number ofpixels in the horizontal direction of the edge image.

The edge list 190 includes a characteristic point flag fea, verticaledge intensity Ev, horizontal edge intensity Eh, synthesis edgeintensity Em, and a removal edge flag sha. The edge list 190 isgenerated by the removal candidate edge extraction unit 51, thepair-edge exclusion unit 53, and the pair exclusion removal unit 55, forexample. Note that the vertical edge intensity Ev is calculated using,for example, the above-mentioned filter Sv, for each pixel. Thehorizontal edge intensity Eh is calculated using, for example, theabove-mentioned filter Sh, for each pixel. In addition, here, thesynthesis edge intensity Em is calculated with the following expression5.

Em=SQRT(Eh ² +Ev ²)  (Expression 5)

The characteristic point flag fea is information indicating whether ornot the synthesis edge intensity Em is more than or equal to apredetermined threshold. For example, when the synthesis edge intensityEm is more than or equal to the threshold, it is considered that thecharacteristic point flag fea=“1”, and the vertical edge intensity Evand the horizontal edge intensity Eh are registered in the edge list190. When the synthesis edge intensity Em is less than the threshold, itis set as the characteristic point flag fea=“0”. With the differentialprocessing as described above, an edge image representative of intensitychange, such as the edge image 20, for example, may be obtained.

The removal edge flag sha is information indicating whether or not acorresponding edge is the removal target edge. When it is judged thatthe corresponding edge is the removal target edge, the removal edge flagsha=“1”, for example. When the corresponding edge is not the removaltarget edge, the removal edge flag sha=“0”, for example. The edge list190 is stored in the storage unit 57, for example.

Note that the edge list 190 is one example, and an edge list is notlimited to this edge list 190. For example, the characteristic pointflag fea may be dispensed with. When there is the characteristic pointflag fea, processing time may be reduced in processing to be describedbelow when data processing of which is dispensed with depending on thecharacteristic point flag fea is present.

Whether or not the corresponding edge is the removal target edge isjudged by the edge removal unit 30 based on the vertical edge intensityEv and the horizontal edge intensity Eh in the edge list 190, and aposition of an edge on the image, for the edge image 20, for example. Anexample of a method for judging a removal candidate edge is describedhereinafter.

When it is assumed that the road surface projection image coordinatesand the center coordinates of the road surface projection image are(x,y) and (cx, cy) respectively, a position vector p from the origin Oof the mobile body coordinate is expressed as follows.

p=(p(0),p(1))  (Expression 6)

p(0)=x−cx  (Expression 7)

p(1)=−(y−cy)  (Expression 8)

In addition, an edge direction vector g whose brightness changedirection viewed in the radial direction is from darkness to bright isexpressed as follows.

g=(g(0),g(1))  (Expression 9)

g(0)=Eh  (Expression 10)

g(1)=−Ev  (Expression 11)

When it is assumed that angle made by the position vector p and the edgedirection vector g is angle ψ, the angle ψ is expressed with the vectorp and the vector g by the following expression 20. Note that the angle ψis also referred to as edge gradient ψ.

$\begin{matrix}{{\cos \; \Psi} = {\frac{p \cdot }{{p}{}} = \frac{{{p(0)}{(0)}} + {{p(1)}{(1)}}}{\sqrt{{p(0)}^{2} + {p(1)}^{2}}\sqrt{{(0)}^{2} + {(1)}^{2}}}}} & \left( {{Expression}\mspace{14mu} 12} \right)\end{matrix}$

Here, when the edge gradient ψ is within a predetermined angle range, anedge change direction viewed from the mobile body coordinate origin O inthe radial direction is the darkness-brightness direction from darknessto brightness. Thus, the removal candidate edge extraction unit 51judges that the corresponding edge is the removal candidate edge. Forexample, a range of the edge gradient ψ for determining as the removalcandidate edge is such that −th1≦ψ≦+th1. The angle th1 may be positiveangle of equal to or less than 90 degrees, and may be 45 degrees by wayof example.

When the brightness change direction in an edge is thedarkness-brightness direction in the edge list 190, the removalcandidate edge extraction unit 51 indicates that the edge is the removalcandidate edge, by making the removal edge flag sha=“1” for the edge,for example. Then, the removal candidate edge extraction unit 51 setsthe edge intensity Em of the edge to a positive value. When thebrightness change direction in the edge is not the darkness-brightnessdirection in the edge list 190, the removal candidate edge extractionunit 51 sets the removal edge flag sha=“0”, for example. Then, theremoval candidate edge extraction unit 51 sets the edge intensity Em ofthe edge to a negative value. The removal candidate edge is extracted asdescribed above, for example.

FIG. 15 is a view illustrating one example of a predetermined areasetting method. A predetermined area is an area on which judgment ismade to exclude an edge from removal candidate edge when the removalcandidate edge is the edge associated with a white line. Thepredetermined area is set by the pair-edge exclusion unit 53 for theremoval candidate edge extracted by the removal candidate edgeextraction unit 51, based on a position of the removal candidate edgeand a direction from the center of a road surface projection image. Thepredetermined area is set for the removal candidate edge for which theremoval edge flag sha=“1”, for example, in the edge list 190. Theremoval candidate edge which is a target of setting of the predeterminedarea is referred to as a set edge.

Assume that coordinates of a set edge on a road surface projection imageare coordinates (x,y). Assume that center coordinates of a predeterminedarea are coordinates (rcx, rcy), and the predetermined area is arectangular area having size of rx×ry. Assume that a distance r is avalue for which the set edge is included in the predetermined area. Inaddition, the size rx×ry is preferably size that is determined based onwidth of a white line, for example. For example, length rx and length rymay be set to about twice of the width of the white line, for example.For example, it may be provided that a white line, which is one of whitepatterns, includes edges which are parallel lines and that the linewidth is 10 to 20 centimeters. In such a case, r may be r=10 cm, by wayof example, and rx and ry may be rx=ry=40 cm, by way of example.However, r, rx, and ry may not be limited to this.

In addition, a white line is brighter (intensity is higher) than theroad surface, and has uniform brightness. Thus, a brightness-darknesspattern of an edge associated with the white line is thedarkness-brightness direction and brightness-darkness direction whenviewed from the center of a mobile body, and an amount in change ofbrightness in each of the darkness-brightness direction andbrightness-darkness direction is almost same. In contrast in theneighborhood of a self-shade, there is no combination of brightness anddarkness, and only the brightness-darkness direction pattern. Assumethat angle 6 is positive angle of less than or equal to 90 degrees madeby a straight line connecting the center of the road surface projectionimage with the coordinate (x,y) of the set edge and the y axis.

Then, the predetermined areas are set as follows.

rcx=x+r×sin δ×sx  (Expression 13)

rcy=y+r×cos δ×sy  (Expression 14)

However, the variables sx, sy are given under the following conditions(A) to (D).

0≦x<cx,0≦y<cy:sx=−1,sy=−1  (A)

cx≦x<wx,0≦y<cy:sx=+1,sy=−1  (B)

0≦x<cx,cy≦y<wy:sx=−1,sy=+1  (C)

cx≦x<wx,cy≦y<wy:sx=+1,sy=+1  (D)

The above conditions vary depending on definitions of the coordinateaxis and the angle δ. The predetermined area in the second embodiment isset as a predetermined area which is on the straight line in the radialdirection, connecting the center of the road surface projection imagewith the corresponding set edge, and which has, as the center, a pointwhich is away from the center of the road surface projection image.

In the example of FIG. 15, a road surface projection image 192 isdivided to four areas of same dimensions relative to the center 191.Each area is an area corresponding to the above conditions (A) to (D).An edge 193 is in an area corresponding to the condition (A). Forexample, when the edge 193 is a set edge, a predetermined area 195 isdefined as a predetermined area 195 with respect to the center 194 whichis calculated by the expression 13 and the expression 14. Here, adistance r is a distance from the edge 193 to the center 194, anddetermined in advance so that the edge 193 is included in thepredetermined area 195.

In this predetermined area, it is judged whether or not thecorresponding set edge is a pair-edge. More specifically, when thepredetermined area includes a white line, it is considered that adarkness-brightness direction edge and a brightness-darkness directionedge are present at a ratio close to 1 to 1, for example, and make apair. Thus, it is expected that a value of average edge intensity Eaveconsidering the change direction of the brightness or the darkness inthe predetermined area is small. Then, when the average edge intensityEave in the predetermined area is smaller than a predeterminedthreshold, it is judged that the corresponding set edge is a pair-edgeconnected to the white line and excluded from the removal candidateedges. Then, the pair-edge exclusion unit 53 makes the removal edge flagsha=“0”, for example, in the edge list 190, for the set edge that isjudged to be the pair-edge.

Note that a predetermined area is not limited to the above. It ispreferable that a predetermined area includes a set edge and is close tothe set edge, and the maximum width of the predetermined area is largerthan width of a white line. In addition, a shape of the predeterminedarea is not limited to a rectangle and may be of other shape. When thepredetermined area is rectangular, the above each side is parallel tothe vertical axis or the horizontal axis of the edge image. However, forexample, the predetermined area may be a rectangle having sides parallelto the brightness change direction in the set edge. In addition, thepredetermined area may be an integral number of areas into which theedge image is divided.

The pair-edge exclusion unit 53 calculates the average edge intensityEave with the following expression 15 by, for example, calculating a sumof the edge intensities Em of edge points in the predetermined area andnormalizing the number of edges k (k is an integer of more than or equalto 1) having the edge intensities.

Eave=ΣEm/k  (Expression 15)

When this average edge intensity Eave is equal to or less than athreshold, it is judged that a darkness-brightness direction edge(positive value) and other edge (negative edge) are mutually offset.More specifically, the pair-edge exclusion unit 53 judges that the setedge is a pair-edge. Then, by setting size of the predetermined area toa value corresponding to width size of the white line, it is judged thatthe pair-edge is the edge associated with the white line. The valuecorresponding to the width size of the white line may be size in whichstandard white line width may fit in a predetermined area, for example.When a predetermined area is a square, the value corresponding to thewidth size of the white line may be a value determined based on widthsize, such as making length of one side the integral multiple of thestandard white line width. Thus, the pair-edge exclusion unit 53 mayavoid removal of a white line pattern on a road surface, by excludingpair-edges from removal candidate edges.

For example, a range of the average edge intensity for judging on apair-edge may be a range that meets the following expression 16, for theedge intensity Em of the set edge.

Eave≦Em/4  (Expression 16)

In the edge list 190, the removal edge flag sha is informationindicating whether or not an edge is an edge corresponding to theremoval target edge image 150. Thus, the pair-edge exclusion unit 53judges that a set edge which meets the expression 16 as a pair-edge, andmakes the corresponding removal edge flag sha=“0”. When the set edge isnot a pair-edge, the pair-edge exclusion unit 53 makes the correspondingremoval edge flag sha=“1”. The movement amount estimation device 50generates each edge list 190 in the first image P1 and the second imageP2 which are taken at two different clock times, as described above. Thegenerated edge list 190 is information corresponding to thepair-exclusion-edge-removed image 155.

Processing in the movement amount estimation device 50 is furtherdescribed hereinafter with reference to FIG. 16 to FIG. 19. FIG. 16 is aflow chart illustrating one example of main operations of the movementamount estimation device 50 according to the second embodiment. Asillustrated in FIG. 16, a picture or an image is inputted to themovement amount estimation device 50 (S201). Then, camera imagesacquired by at least one camera provided on a mobile body at twodifferent clock times are used, as described above. Assume that an edgeimage is generated by performing differential processing on each roadsurface projection image generated based on each camera image (S202).

The removal candidate edge extraction unit 51 performs a removalcandidate edge extraction process (S203). Details are described belowwith reference to FIG. 17. The pair-edge exclusion unit 53 performs aprocess to exclude an edge, as a pair-edge, associated with a white linedrawn on a road surface (S204). Details are described below withreference to FIG. 18. The pair exclusion removal unit 55 performs aprocess to remove from the edge images removal target edges from whichthe pair-edges are removed (S205). Details are described below withreference to FIG. 19.

A movement distance is estimated using the edge images which areoutputted from the movement amount estimation device 50 and from which aself-shade is removed based on the images taken at two different clockimages (S206). When an instruction to end is not inputted (S207: NO),processing is repeated from S201. When the instruction to end isinputted (S207: YES), the processing ends.

FIG. 17 is a flow chart illustrating one example of a removal candidateedge extraction process according to the second embodiment. Asillustrated in FIG. 17, the removal candidate edge extraction unit 1sets the variable i=0 (S211). When the variable i is not i<wy (wy is thenumber of pixels in the y direction of the road surface projectionimage) (S212: NO), the removal candidate edge extraction unit 51 returnsthe processing to the processing of FIG. 16. When the variable i is i<wy(S212: YES), the removal candidate edge extraction unit 51 sets thevariable j=0 (S213).

When the variable j is not j<wx (wx is the number of pixels in the xdirection of the road surface projection image) (S214: NO), the removalcandidate edge extraction unit 51 sets i=i+1 (S215) and returns theprocessing to S212. When the variable j is j<wx (S214: YES), the removalcandidate edge extraction unit 51 judges in the edge list 190corresponding to the pixel whether or not the characteristic point flagfea=“1” (S216).

When the characteristic point flag is not fea=“1” (S216: NO), theremoval candidate edge extraction unit 51 shifts the processing to S221.When the characteristic point flag is fea=“1” (S216: YES), the removalcandidate edge extraction unit 51 calculates edge gradient iv (S217).More specifically, the removal candidate edge extraction unit 51calculates the edge gradient ψ based on the expression 12.

The removal candidate edge extraction unit 51 judges whether or not thecalculated edge gradient ψ falls within a predetermined angle range thathas already been defined (S218). In this example, it is judged whetheror not the predefined threshold th1 meets −th1≦ψ≦th1. When thepredefined threshold th1 meets the condition in S218 (S218: YES), theremoval candidate edge extraction unit 51 sets the removal edge flagsha=“1” in the edge list 190. In addition, the removal candidate edgeextraction unit 51 calculates the synthesis edge intensity Em from theabove-mentioned expression 5 and stores the synthesis edge intensity Emin the edge list 190 (S219).

When the predefined threshold th1 does not meet the condition in S218(S218: NO), the removal candidate edge extraction unit 51 sets theremoval edge flag sha=“0” in the edge list 190. In addition, the removalcandidate edge extraction unit 51 calculates the synthesis edgeintensity Em from the following expression 17a and stores the synthesisedge intensity Em in the edge list 190 (S220).

Em=−SQRT(Eh ² +Ev ²)  (Expression 17a)

In S221, the removal candidate edge extraction unit 51 sets j=j+1 andreturns the processing to S214. With the processing described above, theremoval candidate edge is extracted.

FIG. 18 is a flow chart illustrating one example of a pair-edgeexclusion process according to the second embodiment. The pair-edgeexclusion unit 53 sets the variable i=0 (S231). When the variable i isnot i<wy (wy is the number of pixels in the y direction of the roadsurface projection image) (S232: NO), the pair-edge exclusion unit 53returns the processing to the processing of FIG. 16. When the variable iis i<wy (S232: YES), the pair-edge exclusion unit 53 sets the variablej=0 (S233).

When the variable j is not j<wx (wx is the number of pixels in the xdirection of the road surface projection image) (S234) NO), thepair-edge exclusion unit 53 sets i=i+1 (S235) and returns the processingto S232. When the variable j is j<wx (S234: YES), the pair-edgeexclusion unit 53 judges in the edge list 190 corresponding to the edgewhether or not the removal edge flag sha=“1” (S236).

When the removal edge flag corresponding to the edge is not sha=“1”(S236: NO), the pair-edge exclusion unit 53 shifts the processing toS242. When the removal edge flag corresponding to the edge is sha=“1”(S236: YES), the pair-edge exclusion unit 53 sets a predetermined area(S237). More specifically, the pair-edge exclusion unit 53 sets apredetermined area based on, for example, a position of the edge (any ofthe areas (A) to (D)) and the expressions 13 and 14. The pair-edgeexclusion unit 53 further calculates an average value Eave of the edgeintensities in the predetermined area, based on the expression 15, forexample (S238).

The pair-edge exclusion unit 53 judges whether or not the calculatedaverage value Eave of the edge intensities is less than or equal to apredetermined value that has been already defined (S239). In thisexample, for example, it is judged whether or not the expression 16 ismet. When the expression 16 is met (S239: YES), the pair-edge exclusionunit 53 sets the removal edge flag sha=“0” in the edge list 190 (S240).This means that it is then judged the corresponding set edge is apair-edge.

When the expression 16 is not met (S239: NO), the pair-edge exclusionunit 53 shifts the processing to S241. It is judged that the set edge isthe removal target edge. In S241, the pair-edge exclusion unit 53 setsj=j+1 and returns the processing to S234. With the processing describedabove, the process to exclude pair-edges is performed.

FIG. 19 is a flow chart illustrating one example of a removal targetedge removal process according to the second embodiment. In the secondembodiment, the removal target edge removal process is a process toremove from edge images removal candidate edges (removal target edges)from which pair-edges are removed. The pair exclusion removal unit 55sets the variable i=0 (S251). When the variable i is not i<wy (wy is thenumber of pixels in the y direction of the road surface projectionimage) (S252: NO), the pair exclusion removal unit 55 returns theprocessing to the processing of FIG. 16. When the variable i is i<wy(S252: YES), the pair exclusion removal unit 55 sets the variable j=0(S253).

When the variable j is not j<wx (wx is the number of pixels in the xdirection of the road surface projection image) (S254: NO), the pairexclusion removal unit 55 sets i=i+1 (S255) and returns the processingto S252. When the variable is j<wx (S254: YES), the pair exclusionremoval unit 55 sets the synthesis edge intensity Em=|Em| in the edgelist 190 corresponding to the edge (S256).

When the removal edge flag is not sha=“1” (S257: NO), the pair exclusionremoval unit 55 shifts the processing to S259. When the removal edgeflag is sha=“1” (S257: YES), the pair exclusion removal unit 55 sets thesynthesis edge intensity Em=0 (S258). In S259, the pair exclusionremoval unit 55 sets j=j+1 and returns the processing to S254. The edgelist 190 after this processing is performed is, for example, informationcorresponding to the pair-exclusion-edge-removed image 155. As describedabove, a process to exclude the pair-edge from the removal candidateedge is performed to obtain the removal target edge.

As described above in detail, with the movement amount estimation device50 according to the second embodiment, the removal candidate edgeextraction unit 51 extracts removal target edges based on the brightnesschange direction judged in the edge images. The pair-edge exclusion unit53 judges whether or not the set edge is a pair-edge in a predeterminedarea which includes the removal candidate edge to be the set edge and isclose to the set edge. A pair-edge is a set edge that is judged to beassociated with an edge in a relation of being mutually offset in apredetermined area. Whether or not a set edge is a pair-edge is judgedby, for example, whether or not average edge intensity considering thebrightness change direction is less than or equal to a predeterminedvalue in a predetermined area. The pair-edge is construed as an edgeassociated with a white line on a road surface projection image.

The pair-edge exclusion unit 53 excludes the pair-edge from the removalcandidate edges. The pair exclusion removal unit 55 sets the removalcandidate edges in which a pair-edge is removed as the removal targetedges and removes the removal target edges from the edge image. Themovement amount estimation device 50 outputs an estimated movementamount based on a first pair-exclusion-edge-removed image and a secondpair-exclusion-edge-removed image which are based on the images taken atmutually different clock times.

As described above, with the movement amount estimation device 50according to the second embodiment, in addition to the effect of themovement amount estimation device 1 according to the first embodiment, amovement amount may be estimated with higher accuracy because an edgeassociated with a white line on a road surface is excluded from removaltarget edges. More specifically, when a movement amount is estimated,any effect of a self-shade may be removed while leaving the edgeassociated with the white line on the road surface. Thus, a movementamount may be estimated with high accuracy and in a stable manner, evenwhen a self-shade by illuminating light (sunlight) is imaged. As such,in an image on which a movement amount is estimated, since a self-shadeof a mobile body may be removed while leaving a white line on a roadsurface, excessive removal of road surface patterns may be avoided andaccuracy of the movement amount estimation may be increased.

Variation Example of the Second Embodiment

A variation example of the second embodiment is described hereinafter.Same signs are assigned to a configuration and operation similar to thefirst embodiment or the second embodiment and an overlapping descriptionis omitted. The variation example is another example of the method forsetting a predetermined area in the second embodiment. Here, only themethod for setting a predetermined area is described.

FIG. 20 is a view illustrating a predetermined area setting exampleaccording to a variation example of the second embodiment. Apredetermined area setting example 260 is a setting example in the roadsurface projection image 120 which includes the white line 122, thewhite line 130, and the other shade 136 in addition to the mobile body82. The darkness-brightness direction edges 124 and thebrightness-darkness direction edges 126 are edges associated with thewhite line 122. The darkness-brightness direction edges 132 and thebrightness-darkness direction edges 134 are edges associated with thewhite line 130. The brightness-darkness direction edges 138 are edgesassociated with the other shade 136.

In the predetermined area setting example 260, predetermined areas 266,272, 278 are set. The predetermined area 266 is set to include a setedge, the removal candidate edge 262 of the self-shade 84 of the mobilebody 82 being as the set edge. The predetermined area 266 is illustratedas an example of a predetermined area whose set edge is not a pair-edge.Assume, for example, that the removal candidate edge 262 is adarkness-brightness edge having edge gradient in a direction fromdarkness to brightness which is expressed by a change direction 264.Then, the predetermined area 266 is set to be a rectangular area havingsides parallel to the change direction 264 and sides perpendicular tothe change direction 264. In the predetermined area 260, when averageedge intensity Eave considering the change direction exceeds apredetermined value, it is judged that the removal candidate edge 262,which is the darkness-brightness edge, includes no edge to be paired inthe predetermined area 260. Then, it is judged that the removalcandidate edge 262 associated with the predetermined area 260 is not apair-edge and becomes a removal target edge.

The predetermined area 272 is set to include a set edge, the removalcandidate edge 268 associated with the white line 122 being as the setedge. The predetermined area 272 is illustrated as an example of apredetermined area whose set edge is a pair-edge. Assume, for example,that the removal candidate edge 268 has edge gradient in a directionfrom darkness to brightness which is expressed by a change direction270. Then, the predetermined area 272 is set to be a rectangular areahaving sides parallel to the change direction 270 and sidesperpendicular to the change direction 270. In the predetermined area272, when the average edge intensity Eave considering the changedirection is less than or equal to the predetermined value, it is judgedthat the removal candidate edge 268, which is a darkness-brightnessedge, is paired with any of brightness-darkness edges in thepredetermined area 272. Then, it is judged that the removal candidateedge 268 associated with the predetermined area 272 is a pair-edge.

The predetermined area 278 is set to include a set edge, the removalcandidate edge 274 associated with the white line 130 being as the setedge. The predetermined area 278 is illustrated as another example ofthe predetermined area whose set edge is a pair-edge. Assume, forexample, that the removal candidate edge 274 has edge gradient in adirection from darkness to brightness which is expressed by a changedirection 276. Then, the predetermined area 278 is set to be rectangulararea having sides parallel to the change direction 276 and sidesperpendicular to the change direction 276. In the predetermined area278, when the average edge intensity Eave considering the changedirection is less than or equal to the predetermined value, it is judgedthat the removal candidate edge 274 is a paired with any of edges in thepredetermined area 278. Then, it is judged that the removal candidateedge 274 associated with the predetermined area 278 is a pair-edge.Similarly, a predetermined area is set for each of extracted removalcandidate edges, and it is judged in each predetermined area whether ornot a set edge is a pair-edge.

As described above, according to the variation example, in addition tothe effect of the movement amount estimation device 1 according to thefirst embodiment and the movement amount estimation device 50 accordingto the second embodiment, pair-edges may be excluded more efficiently.More specifically, in the variation example, since a predetermined areais set according to edge gradient, it is possible to efficiently includea white line in the predetermined area. Thus, a pair-edge may beexcluded from removal candidate edges with higher accuracy, and amovement amount may be estimated with higher accuracy.

Third Embodiment

A movement amount estimation device according to a third embodiment isdescribed hereinafter. Same signs are assigned to a configuration andoperation similar to the first embodiment, the second embodiment, andthe variation example of the second embodiment, and an overlappingdescription is omitted.

The embodiment is another example of the method for setting apredetermined area in the second embodiment and the method for judgingon a pair-edge. The movement amount estimation device according to thethird embodiment includes a configuration similar to the movement amountestimation device 50 according to the second embodiment. Here, themethod for setting a predetermined area and the method for judging on apair-edge are only described.

FIG. 21 is a view illustrating a predetermined area setting exampleaccording to the third embodiment. FIG. 22 is a flow chart illustratingone example of a pair-edge exclusion method in a predetermined areasetting example 280 according to the third embodiment.

As illustrated in FIG. 21, the predetermined area setting example 280 isa setting example in the road surface projection image 120 including thewhite line 122, the white line 130, and the other shade 136, in additionto the mobile body 82. The darkness-brightness direction edges 124 andthe brightness-darkness direction edges 126 are edges associated withthe white line 122. The darkness-brightness direction edges 132 and thebrightness-darkness direction edges 134 are edges associated with thewhite line 130. The brightness-darkness direction edges 138 are edgesassociated with the other shade 136.

In the predetermined area setting example 280, the road surfaceprojection image 120 are divided into the integral multiple areas whichare then made predetermined areas. In the predetermined area settingexample 280, the road surface projection image 120 are divided into 8×8areas. Here, predetermined areas 282, 284, 286, in particular, aredescribed. The predetermined area 282 is a predetermined area includingdarkness-brightness direction edges 86 of the self-shade 84 of themobile body 82. The predetermined area 282 is illustrated as an examplein which edges included in the predetermined area 282 are not judged asa pair-edge. In the predetermined area 282, when average edge intensityEave considering the brightness change direction does not meet thecondition of the expression 16, it is judged that removal candidateedges of the predetermined area 282 are all not a pair-edge and made aremoval target edge.

The predetermined area 284 is set to include the white line 122. Thepredetermined area 284 is illustrated as an example in which an edgeincluded in the predetermined area 284 is judged as a pair-edge. When asum of edge intensities considering the brightness change directionmeets the condition of the expression 16, for example, it is judged thatthe removal candidate edges in the predetermined area 284 are all apair-edge. More specifically, it is judged that the edge intensities areoffset because a darkness-brightness direction edge and abrightness-darkness direction edge are present as a pair in thepredetermined area 284.

The predetermined area 286 is set to include the white line 130. Thepredetermined area 286 is illustrated as an example in which an edgeincluded in the predetermined area 286 is judged as a pair-edge. Whenaverage edge intensity Eave considering the brightness change directionmeets the condition of the expression 16, for example, it is judged thatremoval candidate edges in the predetermined area 286 are all apair-edge. Judgment as described above is made on all predeterminedareas.

FIG. 22 is a view illustrating one example of a pair-edge exclusionmethod according to a variation example of the third embodiment. Thepair-edge exclusion process in this embodiment is described as beingperformed by the pair-edge exclusion unit 53. In addition, this processis performed as the process of S204 of FIG. 16. The pair-edge exclusionunit 53 divides a road surface projection image into rn areas (rn is aninteger of more than or equal to 1) and sets a predetermined area(S291). When a variable l is not l<rn (S292: NO), the pair-edgeexclusion unit 53 returns the processing to the processing of FIG. 16.When the variable l is l<rn (S292: YES), the pair-edge exclusion unit 53sets one predetermined area (S293).

The pair-edge exclusion unit 53 judges whether or not average Eave ofedge intensities is less than or equal to a threshold (for example,whether the expression 16 is met) (S295). When the average edgeintensity Eave is not less than or equal to the threshold (S295: NO),the pair-edge exclusion unit 53 shifts the processing to S297.

When the average edge intensity Eave is less than or equal to thethreshold (S295: YES), the pair-edge exclusion unit 53 sets the removaledge flag sha=“0” for all removal candidate edges in the correspondingpredetermined area, in the edge list 190 (S296). In S297, the pair-edgeexclusion unit 53 sets l=l+1, and returns the processing to S292. Withthe processing described above, the pair-edge exclusion process iscarried out.

As described above in detail, with the movement amount estimation deviceaccording to the third embodiment that is configured to perform thepair-edge exclusion process, predetermined areas are the integral numberof areas into which the road surface projection image 166 is divided.Thus, in addition to the effect of the first and second embodiment, andthe variation example of the second embodiment, there is the effect ofquickly carrying out the pair-edge exclusion process.

Fourth Embodiment

A movement amount estimation device 300 according to a fourth embodimentis described hereinafter. Same signs are assigned to a configuration andoperation similar to the first to third embodiments, and the variationexample of the second embodiment, and an overlapping description isomitted.

FIG. 23 is a view illustrating one example of a hardware configurationof the movement amount estimation device 300 according to the fourthembodiment. As illustrated in FIG. 23, the movement amount estimationdevice 300 is mounted on a mobile body 302, for example. The mobile body302 is a vehicle, for example. The movement amount estimation device 300includes an arithmetic processing device 304, a storage device 306, adisplay device 308, an audio output device 310, and a camera 312 whichare coupled with a bus 314.

The arithmetic processing device 304 is a device configured to performarithmetic operations for controlling operation of the movement amountestimation device 300. The arithmetic processing device 304 may alsoperform a process to control the movement amount estimation device 300by reading and executing a program stored in advance in the storagedevice 306. The storage device 306 is a storage device capable ofreading and writing at any time and configured to store a controlprogram for the movement amount estimation device 300, a time-seriesprojection image DB 328, or the like. The storage device 306 may be adrive device of a storage medium or a portable storage medium. Thestorage device 306 may also temporarily store information in theprocessing performed in the movement amount estimation device 300.

The display device 308 is a display device such as a liquid crystaldisplay device, for example. The display device 308 may perform displayrelated to an estimated movement amount, such as display of a currentposition together with a map, for example. The audio output device 310is an audio output device such as a speaker. The audio output device 310may output sound related to an estimated movement amount, such as outputof sound related to a current position, for example. The camera 312 isan imaging device fixed to a predetermined location of the mobile body302. The camera 312 includes one or more imaging devices.

FIG. 24 is a block diagram illustrating one example of a functionalconfiguration of the movement amount estimation device 300 according tothe fourth embodiment. The movement amount estimation device 300includes the edge removal unit 30 and the output unit 5 similar to themovement amount estimation device 50 according to the second embodiment.In addition, the movement amount estimation device 300 includes apicture input unit 322, a road surface projection image creation unit324, a time-series projection image Data Base (DB) 326, and a movementamount estimation unit 328. These functions are implemented by, forexample, causing the arithmetic processing device 304 to read andexecute a control program stored in the storage device 306. Some of thefunctions of the movement amount estimation device 300 may also beimplemented by an integrated circuit using a semiconductor, for example.

As illustrated in FIG. 24, after having a picture taken by the camera312 inputted and performing predetermined processing, the picture inputunit 322 outputs the picture to the road surface projection imagecreation unit 324. For example, when the inputted picture is an analogpicture, the picture input unit 322 converts the analog picture into adigital picture. When the inputted picture is a color picture, thepicture input unit 322 converts the color picture into a monochromepicture and stores the picture in a predetermined memory of the storagedevice 306. When a plurality of cameras are placed, the picture inputunit 322 performs the predetermined processing on each picture andstores picture data of each in the predetermined memory.

From the picture data stored by the picture input unit 322, the roadsurface projection image creation unit 324 creates a road surfaceprojection image (gray image) corresponding to a case in which a roadsurface is viewed from above. The road surface projection image creationunit 324 extracts an edge on this road surface projection image tocreate an edge image. The road surface projection image creation unit324 stores the edge image in the time-series projection image DB 326 forprocessing of an image to be taken at a next cock time and outputs theedge image to the removal candidate edge extraction unit 51.

Here, details of the processing of the road surface projection imagecreation unit 324 are further described using a coordinate system. In acamera coordinate system of FIG. 13, Assume that coordinates of thepoint 180 on the object are (QX, QY, QZ), angle made by the QZ axis andthe straight line 178 is angle θ, and angle made by the QX axis and astraight line 182 connecting the center 172 with the intersection point176 is angle φ. Assume that coordinates of the center 172 in the cameraimage coordinate system px-py are (vx, vy), coordinates of the point 176on the camera image 170 are (px, py), and a pixel value of pixelscorresponding to the point 176 is pm (px, py). In addition, assume thatactual size on the road surface 162 corresponding to size of one pixelof the road surface projection image 166 illustrated in FIG. 12 in the xaxis direction is MX, and actual size on the road surface 162corresponding to size of one pixel of the road surface projection image166 in the y direction is MY.

Then, a pixel value v of pixels corresponding to a point of thecoordinates (x,y) on the road surface projection image 166 is given bythe following expressions 17 to 25.

$\begin{matrix}{v = {{pm}\left( {{px},{py}} \right)}} & \left( {{Expression}\mspace{14mu} 17a} \right) \\{{px} = {{f \times {\tan (\theta)} \times {\cos (\phi)}} + {vx}}} & \left( {{Expression}\mspace{14mu} 18} \right) \\{{py} = {{f \times {\tan (\theta)} \times {\sin (\phi)}} + {vy}}} & \left( {{Expression}\mspace{14mu} 19} \right) \\{\theta = {\arctan\left( {{sqrt}\left( {{{QX}^{\bigwedge}2} + {{QY}^{\bigwedge}{2/{QZ}}}} \right)} \right.}} & \left( {{Expression}\mspace{14mu} 20} \right) \\{\phi = {\arctan \left( {{QX}/{QY}} \right)}} & \left( {{Expression}\mspace{14mu} 21} \right) \\{{QV} = {\begin{pmatrix}{QX} \\{QY} \\{QZ}\end{pmatrix} = {{Rm}*\left( {U - {Tm}} \right)}}} & \left( {{Expression}\mspace{14mu} 22} \right)\end{matrix}$

The matrix Rm is a rotating matrix of the camera 164 and the vector Tmis a translation vector of the camera 164. Coordinates U (UX, UY)represent coordinates in the road surface coordinate system O-XY at aposition corresponding to the coordinates (x,y) on the road surfaceprojection image 166, and are given by the following expression:

$\begin{matrix}{U = \begin{pmatrix}{UX} \\{UY} \\0\end{pmatrix}} & \left( {{Expression}\mspace{14mu} 23} \right) \\{{UX} = {{MX} \times \left( {x - {cx}} \right)}} & \left( {{Expression}\mspace{14mu} 24} \right) \\{{UY} = {{MY} \times \left( {y - {cy}} \right)}} & \left( {{Expression}\mspace{14mu} 25} \right)\end{matrix}$

By determining from a camera image at each clock time the pixel value vusing the above-mentioned expression 17a to the expression 25, a roadsurface projection image of each clock time is generated. As such, afirst image P1 at a first clock time T1 and a second image P2 at asecond clock time T2 are generated as a road surface projection image.Note that the camera 312 may perform imaging at every predeterminedtime.

In addition, the road surface projection image creation unit 324 furthergenerates an edge image from the road surface projection image. Asdescribed above, an edge image is created by causing a generaldifferential operator such as Sobel to act on a road surface projectionimage, for example.

Processing of the edge removal unit 30 may be made identical to theprocessing described in the second embodiment. The processing in thevariation example of the second embodiment and the third embodiment mayalso be used.

The time-series projection image DB 326 is a database configured tochronologically store images generated by the road surface projectionimage creation unit 324. For example, the time-series projection imageDB 326 holds edge images of the current clock time and of one clock timeago. When the edge image of the current clock time is entered, an edgeimage of clock time prior to the last clock time is discarded to holdthe edge image of the current clock time and the edge time of the oneclock time ago. Note that although the time-series projection image DB326 may store and use an edge image extracted from road surfaceprojection images, pair-exclusion-edge-removed images may be stored inthe time-series projection image DB 326 to reduce the storage capacity.

The movement amount estimation unit 328 matches images of a firstpair-exclusion-edge-removed image based on the image that is taken atone cock time ago (first clock time) and stored in the time-seriesprojection image DB 326 and a second pair-exclusion-edge-removed imagebased on the image that is obtained by the edge removal unit 30 andtaken at a second clock time. More specifically, the movement amountestimation unit 328 matches perturbation of the firstpair-exclusion-edge-removed image and the secondpair-exclusion-edge-removed image, calculates an amount of perturbationat which edge patterns of both images match most, and calculates amovement amount of the mobile body based on the amount of perturbation.The output unit 5 outputs the calculated movement amount.

As an image matching method, a method for calculating a degree ofmatching by performing general image matching, such as Sum of AbsoluteDifference (SAD) may be used. A matching score S when edge intensity ofeach edge of the first pair-exclusion-edge-removed image is E_(1m)(j,i),the edge intensity of each edge of the secondpair-exclusion-edge-removed image is E_(2m)(j+dx, i+dy), and the amountof perturbation is dx,dy is as depicted in the following expression 26.

S=Σ _(i)Σ_(j)(|E _(1m)(j,i)−E _(2m)(j+dx,i+dy)|)  (Expression 26)

While perturbing both edge images in a horizon direction from −DX to DXand in a vertical direction from −DY to DY using the above expression26, the movement amount estimation unit 328 compares both edge images ateach perturbation position and calculates the matching score S. Themovement amount estimation unit 328 makes a perturbation position wherethe calculated matching score S is smallest the best match position.This defines the best perturbation amount (dxm, dym) that gives the bestmatch position. Furthermore, when size on the road surface occupied byone pixel in the road surface projection image is WX in the horizontaldirection and WY in the vertical direction, a movement amount (MX, MY)is expressed as follows.

MX=dxm×WX  (Expression 27)

MY=dym×WY  (Expression 28)

As described above, with the movement amount estimation device 300according to the fourth embodiment, a road surface projection image isgenerated based on an image of the surroundings taken by at least onecamera 312 mounted on a mobile body. An edge image is generated based onthe generated road surface projection image. The edge removal unit 30removes a self-shade of the mobile body based on the brightness changedirection viewed in the radial direction from the center of the mobilebody to the outer side. Then, the edge removal unit 30 not removing fromthe edge image a pair-edge judged to be associated with a white line, apair-exclusion-edge-removed image is generated. In the movement amountestimation device 300, a movement amount of the mobile body is estimatedand outputted based on a first pair-exclusion-edge-removed image and asecond pair-exclusion-edge-removed image based on images taken at twodifferent clock times.

As described above, with the movement amount estimation device 300, whena movement amount is estimated, effect of the self-shade is removedwhile leaving an edge associated with a white line on a road surface.Therefore, even when the self-shade by illuminating light (sunlight) isimaged, a movement amount is estimated with high accuracy and in astable manner.

Here, an example of a computer that is commonly applied to cause thecomputer to perform the operation of the movement amount estimationmethod according to the first to fourth embodiments and the variationexample of the second embodiment is described. FIG. 25 is a viewillustrating one example of a hardware configuration of a standardcomputer. As illustrated in FIG. 25, in a computer 400, a centralprocessing unit (CPU) 402, a memory 404, an input device 406, an outputdevice 408, an external storage device 412, a medium drive device 414, anetwork connection device 418 and the like are connected via a bus 410.

The CPU 402 is an arithmetic processing device configured to controloperation of the computer 400 as a whole. The memory 404 is a storageunit configured to store in advance a program to control the operationof the computer 400 and be used as a working area when executing aprogram. The memory 404 is, for example, a random access memory (RAM), aread only memory (ROM), and the like. The input device 406 is a deviceconfigured to acquire input of various information from users associatedwith content of the manipulation and send the acquired input informationto the CPU 402, when manipulated by a computer user. The input device406 is a keyboard device, a mouse device, for example. The output device408 is a device configured to output a processing result by the computer400 and includes a display device and the like. For example, the displaydevice displays a text or an image according to display data to be sentfrom the CPU 402.

The external storage device 412 is a storage device such as a hard disk,for example, and is a device configured to store various controlprograms executed by the CPU 402 or acquired data and the like. Themedium drive device 414 is a device configured to perform reading andwriting on a portable recording medium 416. The CPU 402 may also performvarious types of control processes by reading and executing apredetermined control program recorded in the portable recording medium416 via the medium drive device 414. The portable recording medium is acompact disk (CD)-ROM, a digital versatile disc (DVD), a UniversalSerial Bus (USB) memory, for example. The network connection device 418is an interface device configured to manage delivery and receipt ofvarious data performed with the outside world by cable or wirelessly.The bus 410 is a communication path configured to connect respectivedevices mentioned above with each other and exchange data.

A program configured to cause the computer to perform the movementamount estimation method according to the first to fourth embodimentsand the variation example of the second embodiment described above isstored in the external storage device 412, for example. The CPU 402performs an action to estimate a movement amount by reading a programfrom the external storage device 412 and executing the program stored inthe memory 404. Then, first, a control program to cause the CPU 402 toperform the movement amount estimation process is created and stored inthe external storage device 412. Then, a predetermined instruction isgiven to the CPU 402 from the input device 406, causing the CPU 402 toread this control program from the external storage device 412 andexecute the control program. In addition, this program may be stored inthe portable recording medium 416.

In addition, the present disclosure is not limited to the embodimentsdescribed above and may adopt various configurations or embodimentswithout departing from the scope of the disclosure. For example, eachexpression mentioned above is an example, and may be changed in a rangethat does not go beyond what each expression means. In addition, for acondition to be used in judgment, the above description is an exampleand may be changed as far as the condition does not depart from thescope. In addition, such a change that the edge removal unit 3 isapplied in place of the edge removal unit 30 according to the fourthembodiment or that the variation example according to the secondembodiment is applied may be acceptable.

As another variation example, a system that inputs to the computer 400via network a picture from at least one camera mounted on a mobile body,and transmit an outputted movement amount to the mobile body via acommunication network may also be conceived.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments of the presentinvention have been described in detail, it should be understood thatthe various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A movement amount estimation device, comprising:an edge removal unit configured to generate a first edge-removed imageby removing a first removal target edge based on a change in brightnessindicated by edge intensity in a direction from an inner side to anouter side of a mobile body from a first edge image, the first edgeimage being generated from a first image taken by an imaging devicemounted on the mobile body, and to generate a second edge-removed imageby removing a second removal target edge based on the change inbrightness indicated by the edge intensity in the direction from theinner side to the outer side of the mobile body from a second edgeimage, the second edge image being generated from a second image takenat a different clock time than the first image; and an output unitconfigured to output a movement amount of the mobile body that isestimated based on the first edge-removed image and the secondedge-removed image.
 2. The movement amount estimation device accordingto claim 1, wherein the first removal target edge and the second removaltarget edge are edges whose brightness change direction viewed in thedirection from the inner side to the outer side of the mobile bodyindicates a change direction from darkness to brightness.
 3. Themovement amount estimation device according to claim 1, wherein the edgeremoval unit further includes: a removal candidate edge extraction unitconfigured to extract a first removal candidate edge that is judged tobe removed based on the brightness change direction viewed in thedirection from the inner side to the outer side of the mobile body inthe first edge image, and to extract a second removal candidate edgethat is judged to be removed based on the brightness change directionviewed in the direction from the inner side to the outer side of themobile body in the second edge image; a pair-edge exclusion unitconfigured to determine whether or not the first removal candidate edgeis a first pair-edge in the first edge image, the first pair-edge beingassociated with an edge in a neighborhood of the first removal candidateedge, a change in brightness of a pair of the edge and the first removalcandidate edge being offset based on the each brightness changedirection and each amount of the change in brightness of the edge andthe first removal candidate edge, to exclude the first pair-edge from afirst set of the first removal candidate edges, and to determine whetheror not the second removal candidate edge is a second pair-edge in thesecond edge image, the second pair-edge being associated with an edge ina neighborhood of the second removal candidate edge, a change inbrightness of a pair of the edge and the second removal candidate edgebeing offset based on the each brightness change direction and the eachamount of the change in brightness of the edge and the second removalcandidate edge, to exclude the second pair-edge from a second set of thesecond removal candidate edges; a pair exclusion removal unit configuredto generate a first pair-exclusion-edge-removed image by removing fromthe first edge image the first set of the first removal candidate edgesin which the first pair-edge is excluded, and to generate a secondpair-exclusion-edge-removed image by removing from the second edge imagethe second set of the second removal candidate edges in which the secondpair-edge is excluded, wherein the output unit outputs the movementamount of the mobile body that is estimated based on the firstpair-exclusion-edge-removed image and the secondpair-exclusion-edge-removed image.
 4. The movement amount estimationdevice according to claim 3, wherein the pair-edge exclusion unit isconfigured to make the first removal candidate edge the first pair-edgewhen average edge intensity considering the change direction is lessthan or equal to a predetermined value in a first predetermined areacorresponding to the first removal candidate edge in the first edgeimage, and to make the second removal candidate edge the secondpair-edge when the average edge intensity considering the changedirection is less than or equal to the predetermined value in a secondpredetermined area including the second removal target edge in thesecond edge image.
 5. The movement amount estimation device according toclaim 4, wherein the first predetermined area is a rectangular area thatincludes one of the first set of the first removal candidate edges andhas sides parallel to edge gradient of the one of the first set of thefirst removal candidate edges, and the second predetermined area is arectangular area that includes one of the second set of the secondremoval candidate edges and has sides parallel to edge gradient of theone of the second set of the second removal candidate edges.
 6. Themovement amount estimation device according to claim 4, wherein thefirst predetermined areas are a plurality of areas into which the firstedge image is divided and the second predetermined areas are a pluralityof areas into which the second edge image is divided.
 7. The movementamount estimation device according to any of claim 1 to claim 6, whereinit is judged that the change direction at one point on the first edgeimage or the second edge image indicates a change from darkness tobrightness when angle made by a vector representative of edge gradientbased on edge intensities in two mutually orthogonal directions at theone point and by the direction from the inner side to the outer side ofthe mobile body falls within a predetermined angle range.
 8. A movementamount estimation method to be performed by a computer, the methodcomprising: generating a first edge-removed image by removing firstremoval target edges based on a change in brightness indicated by edgeintensity in a direction from the inner side to the outer side of amobile body in a first edge image, the first edge image being generatedfrom a first image taken by an imaging device mounted on the mobilebody, and generating a second edge-removed image by removing secondremoval target edges based on the change in brightness indicated by theedge intensity in the direction from the inner side to the outer side ina second edge image, the second edge image being generated from a secondimage taken at a different clock time from the first image; andoutputting a movement amount of the mobile body that is estimated basedon the first edge-removed image and the second edge-removed image. 9.The movement amount estimation method according to claim 8, the firstremoval target edges and the second removal target edges are edges whosebrightness change direction viewed in the direction from the inner sideto the outer side of the mobile body indicates a change direction fromdarkness to brightness.
 10. The movement amount estimation methodaccording to claim 8, further comprising: extracting a first removalcandidate edge that is judged to be removed based on the brightnesschange direction viewed in the direction from the inner side to theouter side of the mobile body in the first edge image, and to extract asecond removal candidate edge that is judged to be removed based on thebrightness change direction viewed in the direction from the inner sideto the outer side of the mobile body in the second edge image;determining whether or not the first removal candidate edge is a firstpair-edge in the first edge image, the first pair-edge being associatedwith an edge in a neighborhood of the first removal candidate edge, achange in brightness of a pair of the edge and the first removalcandidate edge being offset based on the each brightness changedirection and each amount of the change in brightness of the edge andthe first removal candidate edge, excluding the first pair-edge from afirst set of the first removal candidate edges, determining whether ornot the second removal candidate edge is a second pair-edge in thesecond edge image, the second pair-edge being associated with an edge ina neighborhood of the second removal candidate edge, a change inbrightness of a pair of the edge and the second removal candidate edgebeing offset based on the each brightness change direction and the eachamount of the change in brightness of the edge and the second removalcandidate edge, and excluding the second pair-edge from a second set ofthe second removal candidate edges; generating a firstpair-exclusion-edge-removed image by removing from the first edge imagethe first set of the first removal candidate edges in which the firstpair-edge is excluded, and generating a secondpair-exclusion-edge-removed image by removing from the second edge imagethe second set of the second removal candidate edges in which the secondpair-edge is excluded, wherein the output unit outputs the movementamount of the mobile body that is estimated based on the firstpair-exclusion-edge-removed image and the secondpair-exclusion-edge-removed image.
 11. The movement amount estimationmethod according to claim 10, further comprising: making the firstremoval candidate edge the first pair-edge when average edge intensityconsidering the change direction is less than or equal to apredetermined value in a first predetermined area corresponding to thefirst removal candidate edge in the first edge image, and making thesecond removal candidate edge the second pair-edge when the average edgeintensity considering the change direction is less than or equal to thepredetermined value in a second predetermined area including the secondremoval target edge in the second edge image.
 12. The movement amountestimation method according to claim 11, wherein the first predeterminedarea is a rectangular area that includes one of the first removalcandidate edges and has sides parallel to edge gradient of one of thefirst removal candidate edges, and the second predetermined area is arectangular area that includes one of the second removal candidate edgesand has sides parallel to edge gradient of one of the second removalcandidate edges.
 13. The movement amount estimation method according toclaim 11, wherein the first predetermined areas are a plurality of areasinto which the first edge image is divided and the second predeterminedareas are a plurality of areas into which the second edge image isdivided.
 14. The movement amount estimation method claim 8, wherein itis judged that the change direction at one point on the first edge imageor the second edge image indicates a change from darkness to brightnesswhen angle made by a vector representative of edge gradient based onedge intensities in two mutually orthogonal directions at the one pointand by the direction from the inner side to the outer side of the mobilebody falls within a predetermined angle range.
 15. A computer-readablerecording medium storing a movement amount estimation program forcausing an information processing apparatus to execute a procedure, theprocedure comprising: generating a first edge-removed image by removingfirst removal target edges based on a change in brightness indicated byedge intensity in a direction from the inner side to the outer side of amobile body in a first edge image that is generated from a first imagetaken by an imaging device mounted on the mobile body, and generating asecond edge-removed image by removing second removal target edges basedon the change in brightness indicated by the edge intensity in thedirection from the inner side to the outer side of the mobile body in asecond edge image that is generated from a second image taken at adifferent clock time from the first image; and outputting a movementamount of the mobile body that is estimated based on the firstedge-removed image and the second edge-removed image.